Why does an outage at a single exchange lead to a market failure?

Carole Comerton-Forde
c.comerton-forde@unsw.edu.au

School of Banking and Finance
UNSW Sydney

Zhuo Zhong
zhuo.zhong@unimelb.edu.au

Department of Finance
University of Melbourne

31 January 2021

When Euronext suffered an outage for a little under 3 hours on 19 October 2020, the average euro volume traded over the day in Euronext stocks fell by over 40% compared to the average daily volume in the previous week. During the outage itself trading virtually stopped. Given multiple trading venues compete with Euronext for the provision of trading services, why did trading activity decline so dramatically when Euronext went down? Why did traders not simply route their orders to one of the many other trading venues available to them. And what can be done to ensure future outages do not negatively impact trading activity?

In this short paper we aim to provide answers to these questions. We do this by examining the trading and market quality characteristics of Euronext stocks on the outage day, and the days immediately before and after this event. We also compare these characteristics to a set of control stocks in other European markets that did not suffer an outage. We focus on trading activity, bid ask spreads, volatility and price discovery.

Our evidence shows that in the absence of a reference price from Euronext, quoting activity on the Multilateral Trading Facilities (MTFs) decreases and spreads widen significantly. The primary market is critical to pricing and accounts for about 60% of price discovery in Euronext stocks on a typical day. When Euronext is not there to provide a reference price for investors and market makers, trading essentially stops. Algorithms and other trading technology need the primary market reference prices to operate. If competing venues are to successfully provide redundancy in the event of market outages, reforms are needed to ensure that an appropriate reference price can be established and that the market’s price discovery mechanisms continue to function.

Our approach

Our analysis examines trading activity, bid ask spreads, volatility and price discovery. We provide definitions of how we measure these variables in an appendix (see A1).

Relying on the old adage "a picture tells a thousand words" ––– we tell the story of the outage mostly using figures. However, we also undertake formal statistical tests using a difference-in-differences approach. A difference-in-differences approach is an econometric technique used in social sciences to try to mimic the randomised controlled trials used in the hard sciences. This approach exploits "natural experiments" where some observations (treatment) are affected by the experiment and other observations (controls) are not. Our natural experiment is the Euronext outage. The treatment impacts only Euronext stocks. And our control stocks are those that have their primary listing on another European market. The difference-in-difference means that we compare treated and control stocks on the outage day between the outage and non-outage periods. The difference in the treatment and control stocks during the outage period on the outage day, is compared to the difference in the treatment and control stocks during the non-outage period. This approach helps to identify the causal impact of the treatment and mitigate any extraneous factors. We provide further details of this, for the interested reader, in the appendix (see A2).

Sample construction and data

Our analysis considers the period 12 to 23 October, 2020, but in some of our analysis we focus only on the days before and after the outage, and the outage day itself.

We examine stocks included in the STOXX Total Market Index (TMI) that trade on the primary market and the three largest MTFs: Turquoise, Chi-X and BATS, each day of our sample. For simplicity we do not consider the many other venues and mechanisms where trading can be done in Europe. We obtain trade and quote data for these trading venues from Refinitiv’s Datascope.

Our sample includes 567 stocks: 109 of which have their primary listing on a Euronext market and 458 listed on non-Euronext markets. We refer to the non-Euronext sample as the control group. In the appendix, we provide a list of the stocks included in the sample, and their primary listing (see A4).

Our choice to only examine stocks that trade in all four venues, biases our sample toward the most fragmented stocks in the market, which are larger, more active stocks. We make this choice because some of the variables we examine need frequent observations in each venue to be correctly estimated at short horizons. But this choice means that our analysis underestimates the impact of the outage for investors. The less liquid and less fragmented stocks not examined will be more adversely affected by the outage as the primary market is even more important for these stocks.

How did the outage impact on trading volume?

We begin by examining daily euro trading volume for the primary market, Turquoise, Chi-X and BATS. Figure 1 shows the daily trading volume for treatment and control stocks. Trading in Euronext stocks fell by 42% from an average daily value of €38.6m to €22.3m. Figure 1 also shows that Euronext stocks were not the only stocks to that suffer because of the outage. Activity in the control stocks also fell by around 23% from €23.1m to €17.7m. On average the control stocks trade less than the Euronext stocks. The decline in control stock volume on the outage day suggests that there may be a spill-over effect perhaps because short term portfolio trading strategies such as index arbitrage and pairs trading that involve Euronext stocks are not possible when Euronext is down.

Next, we consider the intraday pattern in trading in the Euronext and control stocks on the outage day, and the day before and after the outage. We examine trading between 9:00 and 17:30 Central European Time (CET).$^{[1]}$ We consider only continuous trading and exclude the opening/closing auction. Figure 2 shows that on a typical day, trading activity exhibits a flat U-shape with elevated activity around the open and close of continuous trading.$^{[2]}$ Figure 2 also shows that the Euronext accounts for around 74% of trading on a typical day, followed by 11%, 10% and 5% on Chi-X, BATS and Turquoise respectively. The middle pane of the top panel shows that when Euronext went down, trading also largely evaporated on the MTFs.

What happened to price volatility?

Given there is limited trading activity, what happened to the volatility of prices? Figure 3 reports the intraday volatility in one minute increments for Euronext and control stocks on the outage day, and the day before and after the outage. On a typical day, volatility is highest at the open and near the close of continuous trading and is also elevated at the time the US markets open. Volatility on the primary market is typically lower than on the MTFs, and volatility is on average higher in the control stocks than Euronext stocks.

The middle pane of the upper panel shows that volatility increases dramatically during the market outage. Volatility which is normally in the range of 0.01% to 0.07% increases to 0.1% to 7%. There are also numerous gaps in the volatility estimates due to periods without observations. It is noteworthy that volatility is also elevated in the control stocks on the outage day.

What about bid ask spreads?

Given volume is down, what is happening to liquidity provision measured by bid ask spreads? We examine bid ask spreads measured at one-minute increments across the trading day on each of the four trading venues we consider. The top panel of Figure 4 reports the bid ask spread measured in basis points for each of our four trading venues for the day of the outage and the day before and after. The bottom panel reports the same information for the control stocks.

As expected, spreads show a distinctive reverse J-shape across the trading day. Spreads are wide in the morning due to overnight uncertainty and widen again as the end of trading approaches. On average the primary markets offer significantly tighter spreads than the MTFs. This is true both for Euronext and control stocks. The middle pane of the top panel of Figure 4 shows that on the outage day, spreads blow out when Euronext is down. The average spread rises by about 700% to 1,070%.

Table 1 provide summary statistics for the Euronext and control stocks on the outage and non-outage period of the outage day. During the non-outage period, the average (median) spread for Euronext stocks on Euronext is 21 (15) bps compared to 58 (42) bps on BATS, 66 (29) bps on Chi-X and 78 (50) bps on Turquoise. For the control stocks the spreads are wider on average, but the primary markets also offers tighter spreads than the MTFs, with average (median) spreads of 27 (19) bps. The ordering of the MTFs differs with an average (median) spread of 84 (55) bps on Turquoise, 87 (54) bps on BATS and 87 (42) bps on Chi-X. During the outage period, the average (median) spread for Euronext stocks rises to 495 (332) bps on BATS, on 805 (183) bps Chi-X, and 860 (241) bps on Turquoise. It is worth highlighting that during the outage not all stocks were quoted on the MTFs. Only 69, 42 and 34 stocks were quoted on Chi-X, BATS and Turquoise respectively, whereas on non-outage periods they are quoting 107-108 stocks of the 109 in the sample. In contrast, spreads in the control stocks remain relatively unchanged, with a small decrease in the average and median values.

Table 1: Average 1-min relative spread (in bps) on the outage day
Euronext stocks Control stocks
# Stocks Mean Median # Stocks Mean Median
Period Market
Non-outage period Primary 109 20.7 14.9 458 26.9 19.1
BATS 107 57.6 42.1 436 86.8 53.5
Chi-X 108 65.6 29.1 442 87.1 42.2
Turquoise 107 77.9 50.2 432 83.5 54.8
Outage period Primary 74 29.9 18.5 458 27.9 18.5
BATS 42 495.0 332.7 433 86.5 57.3
Chi-X 69 804.8 182.6 438 89.7 42.9
Turquoise 34 859.5 241.1 427 85.7 57.7

Table 2 reports the results for our difference-in-difference analysis on the outage day. The coefficient for the "Treatment:Outage" captures the difference between treated (Euronext) stocks and control stocks on the outage day during the outage and non-outage periods. The difference in the Euronext and control stocks during the outage period is compared to the difference in the treatment and control stocks during the non-outage period. This shows that after controlling for other factors, the spreads on the MTFs increased by between 450 and 770 bps due to the outage. This provides compelling evidence that when the primary market is down, traders and are less willing to post competitive prices on MTFs, despite them being available for trading.

Table 2: Diff-in-diff for the relative spread
BATS Chi-X Turquoise
Treatment:Outage 459.597*** 769.910** 768.526**
Treatment:Outage_t-stat (3.830) (2.495) (2.414)
Other Controls Stock FE Stock FE Stock FE
Adj. R-squared 0.247 0.075 0.146
No. Observations 1018 1057 1000

$^{**}$ and $^{***}$ denote significance at the 5%, and 1% level.

Does this mean the MTFs do not provide a meaningful contribution to the spread? An analysis of the frequency with which an MTFs offers a better price than the prices offered on the primary market suggests that the answer to this question is no.

Table 3 reports the percentage of time the MTFs are at either the best bid or offer price (BBO) when the primary market is not at these prices. Columns 3 and 4 report this percentage for all MTFs together, and the remaining columns report it for each MTF separately. For Euronext stocks, the MTFs are at the BBO without the primary market offering the same price approximately 23% of the trading day. For the control stocks the MTFs are at the BBO without the primary market slightly less frequently. Chi-X is more likely to be at the BBO without the primary market more often than BATS and Turquoise.

Table 3: Percentage of time an MTF is at the best bid or offer without the primary market being at this price
All BATS Chi-X Turquoise
# Stocks Mean Median Mean Median Mean Median Mean Median
Group Date
Euronext 2020-10-12 109 24.5% 22.7% 8.7% 7.7% 16.0% 14.6% 8.2% 6.9%
2020-10-16 109 24.2% 23.8% 8.1% 5.9% 15.9% 16.7% 7.6% 7.0%
2020-10-20 109 22.9% 22.0% 8.0% 6.5% 14.4% 12.5% 7.4% 6.5%
2020-10-26 109 22.9% 24.0% 7.6% 6.2% 14.6% 15.0% 7.9% 7.0%
Control 2020-10-12 458 19.7% 17.5% 6.5% 5.2% 11.6% 9.5% 7.0% 4.9%
2020-10-16 458 20.2% 18.5% 6.7% 5.1% 12.2% 10.5% 7.1% 4.6%
2020-10-20 458 20.4% 17.6% 6.5% 4.8% 12.4% 9.8% 6.9% 4.7%
2020-10-26 443 19.3% 16.8% 6.2% 4.4% 11.7% 10.0% 6.8% 4.4%

These results show that on a normal trading day, the three main MTFs provide economically meaningful improvements in spreads. Why do they not continue to do this when Euronext goes down? We suspect it is due to the importance of the primary market as a reference price. The primary market is likely to be critical to price discovery, and in its absence, traders are less willing to submit orders. So, for our final analysis we turn our attention to identifying where price discovery occurs on a typical trading day.

Where does price discovery take place?

To examine price discovery, we rely on an econometric technique developed by Professor Joel Hasbrouck in 1995.$^{[3]}$ This approach has been the main tool used in the academic literature to identify the relative contribution of different trading venues to price discovery. We again leave a detailed discussion of the econometrics to the appendix, but put simply, the technique examines the prices observed in each venue, and assigns the proportion of the price innovation variation that is provided by each trading venue.

We report the information share or the contribution of the primary venue to price discovery in Table 4 on the days before and after the outage. This shows that on average for stocks on Euronext, Euronext accounts for around 60% (with the upper bound around 96% and lower bound around 22%) of price discovery. The MTFs account for the remainder. Other primary markets account for marginally higher fractions of price discovery. For details on the information share estimation including the upper and lower bound of the information share, please see appendix A3.

Table 4: The information share of the primary market on price discovery
Sample average Sample average on the upper bound Sample average on the lower bound
Group Date
Euronext 2020-10-12 58% 96% 21%
2020-10-16 61% 97% 24%
2020-10-20 58% 96% 20%
2020-10-26 59% 96% 21%
Control 2020-10-12 61% 93% 29%
2020-10-16 63% 94% 31%
2020-10-20 60% 94% 26%
2020-10-26 61% 93% 29%

The dominance of the primary market in the price discovery process explains why traders rely on it as the reference price. It also offers a potential justification of why many trading algorithms and other systems rely on the primary market in order to operate. However, the consequence of this means that when the primary market suffers an outage, trading will essentially stop.

How can this problem be solved?

Requiring algorithms and other trading systems to be designed to incorporate more than one source of reference price data is one possible solution. Another is to designate an alternative venue (either another primary market or an MTF) as the back-up reference price in the event of an outage. No doubt there are other solutions too. Both potential solutions described, require non-trivial technology work for market participants ––– which is perhaps why this problem exists. Market participants choose not to invest in the technology required to handle infrequent market outages. There is clearly a need for open transparent dialogue among market operators and participants about the scope of the problem, its costs, and the best possible solutions. In the absence of a market-led solution regulatory intervention may be necessary.

Endnotes

$[1]$. We exclude the closing auction from our analysis but note that the closing auction did not occur on 19 October due to another trading system issue.
$[2]$. We report trading activity in logs to make it easy to identify differences in activity across the four venues. The absence of the closing auction is a major problem for investors, but we leave the analysis of this issue to future work.
$[3]$. Hasbrouck, J., 1995, One security, many markets: Determining the contributions to price Discovery. Journal of Finance, 50, 1175–1199.


Appendix

This appendix provides supporting materials to support our analysis. It is organised as follows:

  • A1. Variable definitions
  • A2. Details of the difference-in-difference approach
  • A3. Measuring price discovery
  • A4. Sample stocks

A1. Variable definitions

Our variables are measured in one minute increments between 9:00 and 17:30 CET. Variables are measured separately for the primary market, BATS, Chi-X and Turquoise.

Trading activity is trading volume measured in euros.

Bid ask spreads are the best ask price minus the best bid price divide by the midpoint of the best bid and ask price multiplied by 10,000 to convert to basis points.

Volatility is the standard deviation of one minute midpoint log returns measured in percentage.

Percentage of time at the best bid and offer without the primary market is calculated by observing the best prices (BBO) on each market. If a given MTF is at either the best bid and/or ask price and the primary market is not, this is counted as the MTF being at the best price. We sum up the fraction of time for these events and normalized it by the total time of trading from 9:00 to 17:30 CET.

Price discovery is measured using Hasbrouck information share. See A3 for a brief description and Hasbrouck (1995) for further details.

A2. Details of the difference-in-difference approach

The difference-in-difference regression model for our analysis, specified in levels is as follows:

\begin{align} y = \beta_0 + \beta_1 (Treatment \times Outage) + \beta_2 Treatment + \beta_3 Outage + \text{Stock Fixed Effect} + \epsilon \end{align}

where $y$ is the bid ask spread measured in basis points, $Treatment$ is the treatment variable equal to one if the stock is listed on Euronext, and zero if listed elsewhere in Europe, $Outage$ is the outage indicator equal to one during the Euronext outage and zero otherwise. Including the level $Treatment$ controls for differences between the treatment and control groups. Including the level $Outage$ controls for trends common to both treatment and control groups during the Euronext outage. In addition, we control the stock fixed effect capturing stock-level factors. The variation that remains after those controls is the change in spreads experienced by Euronext stocks relative to the change in spreads for stocks listed in other European market between the outage and non-outage periods. This variation is captured by $\beta_1$ the difference-in-difference estimate.

A3. Measuring price discovery

Hasbrouck (1995) developed a method to determine where price information or price discovery occurs when trading occurs in multiple venues. The approach assumes that there is a common implicit efficient price for a given security across all venues, and sources of variation in the efficient price are attributed to different venues. A venue's contribution to price discovery is its information share, defined as the proportion of the efficient price innovation variance that can be attributed to that venue.

Specifically, if $p_{1,t}, p_{2,t}$ are prices from two venues (say the midquote from the primary market and the average midquote from alternative MTFs) tracking the same asset, then based on the law of one price, $p_{1,t}, p_{2,t}$ should be conintegrated (i.e., the linear combination of $p_{1,t}, p_{2,t}$ is a stationary process). We could capture the joint price dynamics with a vector error correction model (VECM), \begin{align} \Delta p_t = B_0 + B_1\Delta p_{t-1} + B_2\Delta p_{t-2} + ... + B_k\Delta p_{t-k} + \underbrace{\alpha \beta^T p_t}_{\text{the error correction term}} + \epsilon_t, \end{align} where $p_t$ is a vector of prices, i.e., $p_t = \left[\begin{array}{c}p_{1,t} \\ p_{2,t}\end{array}\right]$. In the above equation, $\alpha \beta^T p_t$ is the error correction term capturing the long-run (or equilibrium) dynamics between $p_{1,t}, p_{2,t}$.

Hasbrouck (1995) transforms the VECM model into a vector moving average (VMA) model, \begin{align} \Delta p_t = \sum_{\tau=0}^\infty\Psi_{\tau}\epsilon_{t-\tau}. \end{align} The VMA representation gives the impulse response function subsequent to an arbitrary initial shock (from $\epsilon$). And $\sum_{\tau=0}^\infty\Psi_{\tau}$ captures the cumulative long-run predicted price changes implied by an initial shock.

Let $[\sum_{\tau=0}^\infty\Psi_{\tau}]_*$ denote any row of $\sum_{\tau=0}^\infty\Psi_{\tau}$, the Hasbrouck information share is defined as follows, \begin{align} IS_1 = \frac{d_1^2}{d_1^2 + d_2^2}, \\ IS_2 = \frac{d_2^2}{d_1^2 + d_2^2}, \end{align} where the vector of $d_i = [\sum_{\tau=0}^\infty\Psi_{\tau}]_* L, \text{ for }i = 1,2$, and $L$ is the Cholesky factor of the variance covariance matrix of the VECM, i.e., $\Sigma_\epsilon = LH$.

Given that the ordering of $p_{1,t}, p_{2,t}$ in $p_t$ affects the Cholesky decomposition, one can pertubate the ordering of $p_{1,t}$ and $p_{2,t}$ to obtain the upper and lower bounds of the information share estimate. Specifically, the upper bound for $p_{1,t}$ is obtained when $p_{1,t}$ is the first element in the vector of $p_t$, and the lower bound is obtained when $p_{1,t}$ is the second element of the vector.

A4. Sample stocks

We examine stocks included in the STOXX Total Market Index (TMI) that trade on the primary market and the three largest Multilateral Trading Facilities (MTF): Turquoise, Chi-X and BATS, each day of our sample. Our sample includes 567 stocks: 109 of which have their primary listing on a Euronext market and 458 listed on non-Euronext markets. We refer to the non-Euronext sample as the control group. We report the ISIN, company name, the primary market where the company is listed and a summary variable, with 1 indicating it is a Euronext stock, and 0 indicating it is a control stock. The following table contains the list of our sample stocks.

ISIN Company Name Primary Market MIC Treatment Group
0 PTSON0AM0001 Sonae SGPS SA XLIS 1
1 PTREL0AM0008 REN XLIS 1
2 PTEDP0AM0009 EDP XLIS 1
3 PTALT0AE0002 Altri XLIS 1
4 NL0013267909 Akzo Nobel XAMS 1
5 NL0012969182 Adyen XAMS 1
6 NL0012015705 Just Eat Takeawa XAMS 1
7 NL0011872643 ASR Nederland XAMS 1
8 NL0011794037 Ahold Delhaize XAMS 1
9 NL0011540547 ABN Amro XAMS 1
10 NL0010937066 Grandvision XAMS 1
11 NL0010937058 Intertrust XAMS 1
12 NL0010832176 Argenx XBRU 1
13 NL0010583399 Corbion XAMS 1
14 NL0010273215 ASML Holding XAMS 1
15 NL0000395903 Wolters Kluwer XAMS 1
16 NL0000303709 Aegon XAMS 1
17 NL0000009827 Koninklijke DSM XAMS 1
18 NL0000009538 Philips XAMS 1
19 NL0000009165 Heineken XAMS 1
20 NL0000009082 KPN XAMS 1
21 NL0000008977 Heineken Hld XAMS 1
22 LU1883301340 Shurgard Self XBRU 1
23 LU0088087324 SES SA XPAR 1
24 FR0014000MR3 Eurofins Scient XPAR 1
25 FR0013506730 Vallourec XPAR 1
26 FR0013451333 Jeux XPAR 1
27 FR0013326246 Unibail Rod West XAMS 1
28 FR0013258662 ALD XPAR 1
29 FR0013230612 Tikehau Capital XPAR 1
30 FR0013227113 Soitec XPAR 1
31 FR0013199916 Somfy XPAR 1
32 FR0013176526 Valeo XPAR 1
33 FR0013153541 Maisons Monde XPAR 1
34 FR0012435121 Elis XPAR 1
35 FR0011950732 Elior Grup XPAR 1
36 FR0011675362 Neoen XPAR 1
37 FR0010908533 Edenred XPAR 1
38 FR0010613471 Suez XPAR 1
39 FR0010481960 Arga XPAR 1
40 FR0010451203 Rexel XPAR 1
41 FR0010307819 Legrand XPAR 1
42 FR0010259150 Ipsen XPAR 1
43 FR0010242511 E.D.F. XPAR 1
44 FR0010241638 Mercialys XPAR 1
45 FR0010208488 Engie XPAR 1
46 FR0010040865 Gecina XPAR 1
47 FR0006174348 Bureau Veritas XPAR 1
48 FR0004163111 Genfit XPAR 1
49 FR0004024222 Interparfums XPAR 1
50 FR0000184798 Orpea XPAR 1
51 FR0000130809 Societe Generale XPAR 1
52 FR0000130452 Eiffage XPAR 1
53 FR0000130403 Christian Dior XPAR 1
54 FR0000130213 Lagardere XPAR 1
55 FR0000125585 Casino XPAR 1
56 FR0000125338 Capgemini XPAR 1
57 FR0000124141 Veolia Environ XPAR 1
58 FR0000121725 Dassault Avi XPAR 1
59 FR0000121667 EssilorLuxottica XPAR 1
60 FR0000121485 Kering XPAR 1
61 FR0000121220 Sodexo XPAR 1
62 FR0000121204 Wendel XPAR 1
63 FR0000121147 Faurecia XPAR 1
64 FR0000120859 Imerys XPAR 1
65 FR0000120644 Danone XPAR 1
66 FR0000120628 Axa SA XPAR 1
67 FR0000120321 L'Oreal XPAR 1
68 FR0000120222 CNP Assurances XPAR 1
69 FR0000120073 Air Liquide XPAR 1
70 FR0000073298 Ipsos XPAR 1
71 FR0000064578 Covivio XPAR 1
72 FR0000060303 Covivio Hotels XPAR 1
73 FR0000054470 Ubisoft Ent XPAR 1
74 FR0000053225 Metropole XPAR 1
75 FR0000051732 Atos XPAR 1
76 FR0000050809 Sopra Steria Gr XPAR 1
77 FR0000045072 Credit Agricole XPAR 1
78 FR0000044448 Nexans XPAR 1
79 FR0000039091 Robertet XPAR 1
80 FR0000033219 Altarea XPAR 1
81 FR0000031122 Air France KLM XPAR 1
82 ES0127797019 EDP Renovaveis XLIS 1
83 BE0974362940 Barco XBRU 1
84 BE0974349814 WDP XBRU 1
85 BE0974313455 Econocom XBRU 1
86 BE0974293251 AB Inbev XBRU 1
87 BE0974283153 Mithra XBRU 1
88 BE0974281132 Biocartis Group XBRU 1
89 BE0974259880 D'Ieteren XBRU 1
90 BE0003898187 Sipef XBRU 1
91 BE0003878957 VGP XBRU 1
92 BE0003867844 KBC Ancora XBRU 1
93 BE0003853703 Montea Comm XBRU 1
94 BE0003826436 Telenet Grp Hldg XBRU 1
95 BE0003823409 Finance Tubize XBRU 1
96 BE0003822393 Elia Group XBRU 1
97 BE0003818359 Galapagos XAMS 1
98 BE0003816338 Euronav BE XBRU 1
99 BE0003797140 GBL XBRU 1
100 BE0003755692 Agfa Gevart XBRU 1
101 BE0003746600 Intervest Office XBRU 1
102 BE0003735496 Orange Belgium XBRU 1
103 BE0003699130 Gimv XBRU 1
104 BE0003593044 Cofinimmo XBRU 1
105 BE0003592038 Compagnie Bois XBRU 1
106 BE0003555639 Tessenderlo Gr XBRU 1
107 BE0003470755 Solvay XBRU 1
108 BE0003008019 Bk Nat Belgique XBRU 1
109 SE0014960373 Sweco XSTO 0
110 SE0014781795 Addtech XSTO 0
111 SE0014684528 Kinnevik XSTO 0
112 SE0014401378 AddLife XSTO 0
113 SE0013747870 Electrolux Profe XSTO 0
114 SE0012676336 Heba Fastighets XSTO 0
115 SE0012116390 Nordic Ent Group XSTO 0
116 SE0011205194 Wihlborgs XSTO 0
117 SE0011166941 Epiroc XSTO 0
118 SE0011166628 Atlas Copco XSTO 0
119 SE0011116508 Beijer Ref XSTO 0
120 SE0011090018 Holmen XSTO 0
121 SE0010468116 Arjo XSTO 0
122 SE0010323311 BioArctic XSTO 0
123 SE0010048884 Fagerhult XSTO 0
124 SE0009997018 HMS Networks XSTO 0
125 SE0009922164 Essity XSTO 0
126 SE0009888738 Boozt XSTO 0
127 SE0009664253 Instalco XSTO 0
128 SE0009414576 Oncopeptides XSTO 0
129 SE0009161052 Sagax XSTO 0
130 SE0008321608 Investment Oresu XSTO 0
131 SE0007871363 Vitec Software XSTO 0
132 SE0007691613 Dometic Grp XSTO 0
133 SE0007666110 Attendo XSTO 0
134 SE0007665823 Resurs Holding XSTO 0
135 SE0007640156 Scandic Hotels XSTO 0
136 SE0007491303 Bravida Holding XSTO 0
137 SE0007464888 Karo Pharma XSTO 0
138 SE0007185418 Nobina XSTO 0
139 SE0007158910 Alimak Group XSTO 0
140 SE0007158829 Coor Srvice Mgmt XSTO 0
141 SE0007100607 Svenska Hndlsbnk XSTO 0
142 SE0007100359 Pandox XSTO 0
143 SE0007048020 Collector XSTO 0
144 SE0006887063 Hoist Finance XSTO 0
145 SE0006625471 Dustin Group XSTO 0
146 SE0006593927 Klovern XSTO 0
147 SE0006593919 Klovern XSTO 0
148 SE0006370730 Lifco XSTO 0
149 SE0006288015 Granges XSTO 0
150 SE0005999836 AF Poyry XSTO 0
151 SE0005876968 OEM XSTO 0
152 SE0005851706 IAR Systems XSTO 0
153 SE0005757267 Recipharm XSTO 0
154 SE0005677135 Bufab XSTO 0
155 SE0005127818 Sagax XSTO 0
156 SE0004977692 Platzer Hldg XSTO 0
157 SE0003366871 SAS XSTO 0
158 SE0002626861 Cloetta XSTO 0
159 SE0002591420 Tobii XSTO 0
160 SE0002148817 Hansa Biopharma XSTO 0
161 SE0002133975 Systemair XSTO 0
162 SE0001852419 Lindab Internat XSTO 0
163 SE0001664707 Catena XSTO 0
164 SE0001662230 Husqvarna XSTO 0
165 SE0001634262 Dios Fastigheter XSTO 0
166 SE0000862997 BillerudKorsnas XSTO 0
167 SE0000825820 Lundin Energy XSTO 0
168 SE0000683484 CellaVision XSTO 0
169 SE0000667925 Telia Company XSTO 0
170 SE0000652216 ICA Gruppen XSTO 0
171 SE0000470395 Biogaia XSTO 0
172 SE0000426546 New Wave Group XSTO 0
173 SE0000421273 Knowit XSTO 0
174 SE0000375115 Mycronic XSTO 0
175 SE0000310336 Swedish Match XSTO 0
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